CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

Urdnet:A Cryo-EM Particle Automatic Picking Method

Cryo-Electron Microscopy(Cryo-EM)images are characterized by the low signal-to-noise ratio,low contrast,serious background noise,more impurities,less data,difficult data labeling,simpler image semantics,and relatively fixed structure,while U-Net obtains low resolution when downsampling rate information to complete object category recognition,obtains highresolution information during upsampling to complete precise segmentation and positioning,fills in the underlying information through skip connection to improve the accuracy of image segmentation,and has advantages in biological image processing like Cryo-EM image.This article proposes A U-Net based residual intensive neural network(Urdnet),which combines point-level and pixel-level tags,used to accurately and automatically locate particles from cryo-electron microscopy images,and solve the bottleneck that cryo-EM Single-particle biologicalmacromolecule reconstruction requires tens of thousands of automatically picked particles.The 80S ribosome,HCN1 channel and TcdA1 toxin subunits,and other public protein datasets have been trained and tested on Urdnet.The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION,DeepPicker,and acquire the 3Dstructure of picked particleswith higher resolution.